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The purpose of this study is to investigate managers’ decision-making processes when evaluating suggestions provided by human collaborators or artificial intelligence (AI) systems. We employed the framework of Social Comparison Theory (SCT) in the business context to examine the influence of varying social comparison orientation levels on managers’ willingness to accept advice in their organization.

A survey was conducted on a sample of 192 US managers, in which we carried out an experiment manipulating the source type (human vs AI) and assessing the potential moderating role of social comparison orientation. Results were analyzed using a moderation model by Hayes (2013).

Despite the growing consideration gained by AI systems, results showed a discernible preference for human-generated advice over those originating from Artificial Intelligence (AI) sources. Moreover, the moderation analysis indicated how low levels of social comparison orientation may lead managers to be more willing to accept advice from AI.

This study contributes to the current understanding of the interplay between social comparison orientation and managerial decision-making. Based on the results of this preliminary study that used a scenario-based experiment, future research could try to expand these findings by examining managerial behavior in a natural context using field experiments, or multiple case studies.

This is among the first studies that examine AI adoption in the organizational context, showing how AI may be used by managers to evade comparison among peers or other experts, thereby illuminating the role of individual factors in affecting managers’ decision-making.